Statistical analyses of the effect of a drainage tunnel on landslide hydrogeological characteristics

Wang, Dong‐fei; Xu, Haishun; Wang, Lei; Xian, Wu; Sun, Hongyue

doi:10.1002/hyp.13738

Cited by 9 publications

(5 citation statements)

References 48 publications

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“…Landslides, a critical geohazard, annually affect millions and engender significant socioeconomic impacts (Petley 2012). Slope failures arise from diverse triggers: river erosion (Liu et al 2021), groundwater flux (Wang et al 2020), rainfall (Finlay et al 1997), seismicity (Li et al 2016), and anthropogenic excavation (Pan et al 2022). These elements precipitate abrupt, calamitous earth movements, imperiling lives and assets in upland locales, notably where human expansion and environmental flux intensify (Froude and Petley 2018).…”

Section: Introductionmentioning

confidence: 99%

Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing

Cui,

Tao,

Kou

et al. 2024

Preprint

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Landslide hazards pose a significant threat to lives and infrastructure, especially in mountainous regions like the Three Gorges Reservoir area. While the mechanisms driving landslide initiation and progression in reservoir environments are not fully understood. This study aimed to leverage the capabilities of Sentinel-1 satellite imagery and the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique to detect and monitor potential landslide deformations in Yunyang County, Chongqing, China. We utilized Sentinel-1 data acquired between January 1, 2020, and December 28, 2022, to generate deformation velocity maps. Twelve potential landslides were identified, primarily concentrated near residential areas and along the Yangtze River. Precipitation emerged as the primary driver of surface deformation and landslide initiation, with potential landslides in residential vicinities and along the river exhibiting significantly higher deformation rates during the wet season compared to the dry season. These sites are susceptible to slope failures and geological disasters upon reaching critical antecedent rainfall thresholds, highlighting the necessity for continuous monitoring. Other potential landslides maintained consistent deformation rates across seasons but experienced brief accelerations following heavy precipitation events. Notably, potential landslides adjacent to the Yangtze River experienced accelerated deformation during periods of significant river water level reductions, suggesting that the river's cyclical water level fluctuations influence slope stability. The study demonstrated the effectiveness of SBAS-InSAR in detecting millimetric deformations in incipient landslides, a crucial step in averting landslide disasters and ensuring public safety.

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Section: Introductionmentioning

confidence: 99%

Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing

Cui,

Tao,

Kou

et al. 2024

Preprint

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“…The development of landslides is in uenced by complex internal geological conditions and external factors, such as rainfall, groundwater activity, and earthquakes, making it a multidimensional non-linear process (Hong et al 2005;Tang et al 2022; Wang et al 2020). Therefore, a comprehensive analysis of the various collected slope monitoring data is necessary to accurately predict landslide deformation.…”

Section: Introductionmentioning

confidence: 99%

Construction and Application of Intelligent Landslide Warning System Based on Fusion of Multiple Source Data

Liu,

Xie,

Tang

et al. 2024

Preprint

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Deformation development of landslides is a complex process that usually requires the deployment of various monitoring devices on a slope to obtain data from multiple sources for effective analysis. Only through this process can an accurate judgment be made regarding the entire evolutionary trend of slope deformation. Monitoring data obtained through multiple sensors have a certain degree of correlation, randomness, and redundancy. The effective processing of these data is crucial for monitoring and providing warnings. Given that machine learning is adept at uncovering implicit patterns from heterogeneous data sources, it is well suited for predicting landslide deformation with multi-factor monitoring. The sample dataset forms the foundation for training the models, and the quality and quantity of the dataset directly affect its accuracy and generalization ability. However, significant deformation in landslide bodies is relatively rare, leading to an imbalance in the collected sample dataset. To address this issue, this study proposed the genetic algorithm improved multi-classification-genetic-synthetic minority oversampling technique (SMOTE)-algorithm (GAMCGSA). Building on the multi-classification-genetic-SMOTE-algorithm (MCGSA), it integrated genetic algorithms to determine the optimal sampling rate. Based on this rate, new samples were generated, avoiding the creation of a large number of synthetic samples and effectively addressing the issue of sample imbalance. Subsequently, a convolutional neural network (CNN) was employed to process non-image data from multiple sources, resulting in the development of an intelligent landslide warning model. According to the test results, the F1 score of this model reached 84.2% with an accuracy of 90.8%, surpassing those of traditional machine learning models. Based on the developed warning model and utilizing popular development frameworks, geographic information systems, and database technologies, an intelligent landslide monitoring warning system was constructed. This system integrates intelligent landslide monitoring and warning services, and provides scientific and reliable technical support for landslide disaster prevention and reduction.

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“…The AC/DC converter switch circuit and other related components can be used in the study of the main circuit to ensure that the electronic integration module can be used effectively. Over the past three decades, power electronics has undergone dramatic developments in power devices, control methods, circuit design, computer-aided analysis, passive components, and mounting techniques [8][9][10][11]. The urgent need for practical applications has proved to be a catalyst for the development of power electronics.…”

Section: Introductionmentioning

confidence: 99%

Application of power electronic circuit design based on BP artificial neural network in environmental monitoring

Dong

2023

Applied Mathematics and Nonlinear Sciences

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Our natural resources have played an important role in building our economy. In addition to the number one energy resource, oil, electric energy is also the best quality and much in demand by society in the history of human civilization. It is because electrical energy can be fully developed and applied by society that the construction of human spiritual civilization has entered industrialization and high-tech information technology. The transmission and application of electric energy has been recognized by society, but how can we make it more reasonable and efficient for easy utilization. In order to solve the problem of how complex power electronic circuit devices can be more widely used with improved technology. This paper establishes the error estimation model of intelligent circuit from data collection and data prediction preprocessing, then proposes to optimize the number of nodes in the hidden layer by using particle swarm optimization algorithm to address the limitations of the traditional BP neural network, and uses the optimized number of nodes in the hidden layer to build a BP neural network structure to train the training sample data, and calculates the error data of the intelligent circuit based on the trained BP neural network to test the sample data. The method established in this paper is used to perform the evaluation of intelligent circuit operation errors. The simulation example shows that the established model can effectively evaluate the smart circuit operation error, and the evaluation accuracy is significantly improved compared with the traditional evaluation method.

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Statistical analyses of the effect of a drainage tunnel on landslide hydrogeological characteristics

Cited by 9 publications

References 48 publications

Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing

Hydrological Influences on Landslide Dynamics in the Three Gorges Reservoir Area: An SBAS-InSAR Study in Yunyang County, Chongqing

Construction and Application of Intelligent Landslide Warning System Based on Fusion of Multiple Source Data

Application of power electronic circuit design based on BP artificial neural network in environmental monitoring

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